Parameter adaptive stochastic model predictive control for wind–solar–hydrogen coupled power system

With the increasing global energy scarcity and environmental concerns, the wind–solar–hydrogen (WSH) coupled system has garnered widespread attention as an efficient and eco-friendly renewable energy solution. Addressing the impact of uncertainty on the source and load of the coupled system concerning its power balance, a parameter adaptive stochastic model predictive control (PAS-MPC) power regulation strategy based on the scenario analysis method is proposed herein. In order to characterize the probabilistic information about the uncertainty on both sides of the system, scenario generation and reduction techniques are used to obtain classical scenarios of wind and solar outputs as well as electrical loads as inputs to PAS-MPC. With the aim of optimizing the computational time constant of SMPC, the parameter adaptive method is proposed to change the prediction time domain and control time domain of the system. Simulation results demonstrate the effectiveness of PAS-MPC in addressing uncertainties on the source and load sides of the WSH coupled system. Optimization results reveal that PAS-MPC considerably improves the system power balance compared to SMPC. Compared to the conventional model predictive control (MPC), PAS-MPC effectively mitigates high-power state of the controllable equipment of the system, thereby enhancing its lifecycle.